Medical Care
Home | Data Overview | Multi-Dimensional Analysis | Conclusion
Major | Gender | Career | Ethnicity | First Generation Students | Dependents | Medical Condition | Medical Care | Analysis
Function
To begin, let’s explore the function we created to run all of this statistical analysis:
mysubsetMDS <- function(x){
mysubset <- df %>%
select(starts_with(x))
meta <- metaMDS(mysubset)
MDS_df <- data.frame(MDS1=meta$points[,1],MDS2=meta$points[,2]) %>%
cbind(demo)
return(MDS_df)
}
In English, this function allows us to run the MDS according to each subset of demographic and each subset of question type that we want. Obviously, in this page, we are exploring those who care for those with medical conditions.
Question Types
Science Identity
Let’s explore the science identity subset of questions first. Running our function we created and plotting it, we are left with this image of the plot:
This is great and all, but let’s run an adonis test to see if there is a significant difference in answers from those who care for those with medical conditions and those who don’t:
##
## Call:
## adonis(formula = si ~ demo$med_condition_dependent)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$med_condition_dependent 1 0.0249 0.024915 0.77976 0.00251 0.409
## Residuals 310 9.9050 0.031952 0.99749
## Total 311 9.9299 1.00000This shows that the differences in answers are not significant, according to care of those with medical conditions.
Career Motivation
Next, let’s look into Carer Motivation:
And an Adonis test:
##
## Call:
## adonis(formula = cm ~ demo$med_condition_dependent)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$med_condition_dependent 1 0.1017 0.101709 2.0043 0.00642 0.147
## Residuals 310 15.7311 0.050746 0.99358
## Total 311 15.8329 1.00000This shows that the differences in answers are not significant, according to care of those with medical conditions.
Intrinsic Motivation
Now, Intrinsic Motivation:
And an Adonis test:
##
## Call:
## adonis(formula = im ~ demo$med_condition_dependent)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$med_condition_dependent 1 0.0495 0.049487 1.7508 0.00562 0.164
## Residuals 310 8.7624 0.028266 0.99438
## Total 311 8.8119 1.00000This shows that the differences in answers are not significant, according to care of those with medical conditions.
Self-Determination
Now, Self-Determination:
And an Adonis test:
##
## Call:
## adonis(formula = sd ~ demo$med_condition_dependent)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$med_condition_dependent 1 0.0420 0.041965 1.7174 0.00551 0.171
## Residuals 310 7.5749 0.024435 0.99449
## Total 311 7.6168 1.00000This shows that the differences in answers are not significant, according to care of those with medical conditions.
Self-Efficacy
Now, Self-Efficacy:
And an Adonis test:
##
## Call:
## adonis(formula = se ~ demo$med_condition_dependent)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$med_condition_dependent 1 0.0134 0.013375 0.48822 0.00157 0.577
## Residuals 310 8.4925 0.027395 0.99843
## Total 311 8.5059 1.00000This shows that the differences in answers are not significant, according to care of those with medical conditions.
Grade Motivation
Now, Grade Motivation:
And an Adonis test:
##
## Call:
## adonis(formula = gm ~ demo$med_condition_dependent)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$med_condition_dependent 1 0.0108 0.010751 0.4844 0.00156 0.596
## Residuals 310 6.8801 0.022194 0.99844
## Total 311 6.8909 1.00000This shows that the differences in answers are not significant, according to care of those with medical conditions.
Competency in Science
Now, Competency in Science:
And an Adonis test:
##
## Call:
## adonis(formula = sci_comp ~ demo$med_condition_dependent)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$med_condition_dependent 1 0.0329 0.032870 0.94604 0.00304 0.372
## Residuals 310 10.7711 0.034745 0.99696
## Total 311 10.8040 1.00000This shows that the differences in answers are not significant, according to care of those with medical conditions.
Personal Community Orientation
Now, Personal Community Orientation:
And an Adonis test:
##
## Call:
## adonis(formula = per_comm_orient ~ demo$med_condition_dependent)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$med_condition_dependent 1 0.00024 0.0002436 0.027515 0.00009 0.921
## Residuals 310 2.74434 0.0088527 0.99991
## Total 311 2.74458 1.00000This shows that the differences in answers are not significant, according to care of those with medical conditions.
